{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:21:20Z","timestamp":1774945280608,"version":"3.50.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T00:00:00Z","timestamp":1580688000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T00:00:00Z","timestamp":1580688000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1007\/s00500-020-04734-w","type":"journal-article","created":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T20:03:41Z","timestamp":1580760221000},"page":"13209-13217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["LASSO multi-objective learning algorithm for feature selection"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7868-6968","authenticated-orcid":false,"given":"Frederico","family":"Coelho","sequence":"first","affiliation":[]},{"given":"Marcelo","family":"Costa","sequence":"additional","affiliation":[]},{"given":"Michel","family":"Verleysen","sequence":"additional","affiliation":[]},{"given":"Ant\u00f4nio P.","family":"Braga","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,3]]},"reference":[{"key":"4734_CR1","unstructured":"(1994) Encyclopaedia of Mathematics (set). Springer Netherlands"},{"key":"4734_CR2","volume-title":"Advances in neural information processing systems","author":"PL Bartlett","year":"1997","unstructured":"Bartlett PL (1997) For valid generalization the size of the weights is more important than the size of the network. In: Mozer MC, Jordan MI, Petsche T (eds) Advances in neural information processing systems, vol 9. The MIT Press, Cambridge"},{"key":"4734_CR3","unstructured":"Bland RG, Goldfarb D, Todd MJ (1980) The ellipsoid method: a survey. Technical report, Ithaca"},{"key":"4734_CR4","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/3-540-33019-4_7","volume-title":"Multi-objective machine learning, studies in computational intelligence","author":"A Braga","year":"2006","unstructured":"Braga A, Takahashi R, Costa M, Teixeira R (2006) Multi-objective algorithms for neural networks learning. In: Jin Y (ed) Multi-objective machine learning, studies in computational intelligence, vol 16. Springer, Berlin, pp 151\u2013171"},{"key":"4734_CR5","doi-asserted-by":"publisher","DOI":"10.1037\/10037-000","volume-title":"Perception and communication","author":"D Broadbent","year":"1958","unstructured":"Broadbent D (1958) Perception and communication. Pergamon Press, London"},{"key":"4734_CR6","unstructured":"Dua D, Graff C (2017) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"4734_CR7","doi-asserted-by":"crossref","unstructured":"Gacek A, Pedrycz W (2011) ECG signal processing, classification and interpretation: a comprehensive framework of computational intelligence. Springer, New York","DOI":"10.1007\/978-0-85729-868-3"},{"key":"4734_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/neco.1992.4.1.1","volume":"4","author":"S Geman","year":"1992","unstructured":"Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias-variance dilemma. Neural Comput 4:1\u201358","journal-title":"Neural Comput"},{"key":"4734_CR9","doi-asserted-by":"crossref","unstructured":"Gretton A, Bousquet O, Smola A, Sch\u00f6lkopf B (2005) Measuring statistical dependence with Hilbert\u2013Schmidt norms. In: International conference on algorithmic learning theory. Springer, pp 63\u201377","DOI":"10.1007\/11564089_7"},{"key":"4734_CR10","first-page":"1","volume-title":"Practical feature selection : from correlation to causality","author":"I Guyon","year":"2008","unstructured":"Guyon I, Road C (2008) Practical feature selection : from correlation to causality. IOS Press, Amsterdam, pp 1\u201317"},{"key":"4734_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: data mining, inference and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and prediction, 2nd edn. Springer, New York","edition":"2"},{"key":"4734_CR12","unstructured":"Haykin S (2001) Redes Neurais. Princ\u00edpios e Pr\u00e1tica, Bookman"},{"key":"4734_CR13","first-page":"129","volume-title":"AAAI","author":"K Kira","year":"1992","unstructured":"Kira K, Rendell LA (1992) The feature selection problem: traditional methods and a new algorithm. AAAI. MIT Press, Cambridge, pp 129\u2013134"},{"key":"4734_CR14","unstructured":"Rampone S, Russo C (2012) A fuzzified brain algorithm for learning DNF from incomplete data. Electron J Appl Stat Anal 5(2). http:\/\/siba-ese.unisalento.it\/index.php\/ejasa\/article\/view\/11409"},{"key":"4734_CR15","unstructured":"Teixeira BTRS Roselito (2000) Improving generalization of MLPS with multi-objective optimization. Neurocomputing 35(1\u20134):189\u2013194"},{"key":"4734_CR16","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996a) Regression shrinkage and selection via the lasso. J R Stat Soc B 58:267\u2013288","journal-title":"J R Stat Soc B"},{"key":"4734_CR17","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996b) Regression shrinkage and selection via the lasso. J R Stat Soc (Ser B) 58:267\u2013288","journal-title":"J R Stat Soc (Ser B)"},{"key":"4734_CR18","unstructured":"Vapnik V, Boser (1992) A training algorithm for optimal margin classifiers. In: Fifth annual workshop on computational learning theory, San mateo, pp 1\u2013152"},{"key":"4734_CR19","first-page":"273","volume":"20","author":"VN Vapnik","year":"1995","unstructured":"Vapnik VN, Cortes C (1995a) Support vector networks. Mach Learn 20:273\u2013297","journal-title":"Mach Learn"},{"key":"4734_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"1995","unstructured":"Vapnik V (1995b) The nature of statistical learning theory. Springer, New York"},{"issue":"1","key":"4734_CR21","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1162\/NECO_a_00537","volume":"26","author":"M Yamada","year":"2014","unstructured":"Yamada M, Jitkrittum W, Sigal L, Xing EP, Sugiyama M (2014) High-dimensional feature selection by feature-wise kernelized lasso. Neural Comput 26(1):185\u2013207. https:\/\/doi.org\/10.1162\/NECO_a_00537 pMID: 24102126","journal-title":"Neural Comput"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-04734-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-020-04734-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-04734-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:43:59Z","timestamp":1722386639000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-020-04734-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,3]]},"references-count":21,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["4734"],"URL":"https:\/\/doi.org\/10.1007\/s00500-020-04734-w","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,3]]},"assertion":[{"value":"3 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}